FW moderates a discussion on improving decision-making and increasing value using Big Data analytics between Shanji Xiong at Experian DataLabs, Ken Elliott at HP and Shaheen Dil at Protiviti.

FW: To what extent are you seeing an increased demand for Big Data analytics in today’s business environment? What overarching advantages does it offer to companies?

Dil: Many organisations have made fundamental investments in Big Data infrastructures and capabilities and are now actively exploring the best ways to achieve return on these investments. Applications range from customer behaviour to people analytics, from ways to better understand risk to achieving operational excellence. As one would expect, these use cases vary greatly by industry. The consumer retail sector, for example, leads the pack in use of analytics to understand the customer domain, whereas financial services companies, banks and insurers have greatly advanced their ability to model risk. We are seeing an increased demand for analytics services from the companies that have narrowed their focus on specific uses, such as risk management, as it is easier to quantify return on investment in those cases. The advantages that these companies are realising are in line with many of the promises of Big Data – increased higher-quality input into decision-making processes from a variety of internal and external, structured and unstructured data.

Elliott: The volume and variety of data coming into an organisation in various forms is continuing to explode and an increasing number of companies have more data than they can effectively analyse and exploit with traditional methods. Whether or not you call it ‘Big Data,’ taking advantage of this data requires new approaches in how this data is collected, stored, analysed, archived and governed. Data holds insights into business factors and customer behaviours and companies that first harness this data are able to gain a competitive advantage over those that do not.

Xiong: According to Forbes, over the 12 month period of 2014, the demand for computer system analysts with Big Data expertise increased 89.9 percent and 85.4 percent for computer and information research scientists respectively. This highlights that organisations from all industries continue to invest in Big Data analytics to maintain and improve their competitive advantage. They need to be able to sift through large amounts of data, find patterns and distil the key takeaways in order to make better decisions, improve our society and in turn, drive our economy forward.

Big Data helps to prove more of the ‘why’ behind events discovered with traditional analytics, and this added dimension greatly aids in decision-making.

— Shaheen Dil

FW: In what ways does the use of Big Data analytics deliver demonstrable results for businesses that conventional analytics and business intelligence solutions cannot? How does this translate into improved decision-making?

Elliott: Traditional business intelligence solutions are highly structured and often focus on standardised reporting of internally available data. These solutions are well-suited for ‘referential’ analytics where the reporting of facts is critical – such as in finance or regulatory compliance – and focus more on ‘what’ has happened versus ‘why’. Big Data often originates from machines, sensors, logs, social interactions, audio, rich media and more. These sources often contain insights into ‘why’ things happen and what is potentially around the corner. Big Data analytics techniques can mine through massive amounts of all types of data to find hidden insights that would not have been possible with traditional methods.

Xiong: The intelligent use of data assets helps businesses make better decisions. With it we can prevent fraud, verify identity, manage debt, and retain and expand customer relationships. Those businesses that fuel our economy can also use it to plan, target and execute strategies of all kinds, thus turning data into value-added insight. That’s the real promise of Big Data: giving researchers an unprecedented opportunity to look at their business problems from a fresh perspective and to capture the value hidden within their data assets.

Dil: Even with the advent and adoption of Big Data analytics, we are still seeing conventional analysis and business intelligence solutions as a key portion of the equation. More companies are using Big Data in conjunction with these traditional sources of analytics to help better frame and add additional detail and context to existing analyses. Big Data helps to prove more of the ‘why’ behind events discovered with traditional analytics, and this added dimension greatly aids in decision-making as it helps to design better responses to addressing the required change. But it does not stop there. Predictive capabilities allow for preventive intervention with traditional operating models. How loyal are our clients going to be in the next two quarters? Should we spend $100 to keep a particular client or $150 to let them go? What should be the scope of our next internal audit based on the real-time signals we receive from our data? These questions can be answered using Big Data analytics.

FW: How should a company go about ensuring that their Big Data datasets do not infringe on a third party’s intellectual property or contractual rights? What other potential liabilities exist in this context?

Dil: One of the challenges in launching Big Data is managing risk. Traditional definitions of Big Data have focused on three Vs: Velocity, Variety and Volume. We typically add two more: Veracity and Value. The veracity of data must be managed carefully to ensure that we are not bringing in risk through either intellectual property infringements or privacy and confidentiality concerns. One way to protect an organisation from IP or contractual right risks is to implement robust data governance programs so that organisations understand the definitions and composition of data. The natural inclination to bring everything into a Big Data program must be balanced by caution – just because we can source the data does not mean we should always bring in those data sets. Thus the Value of including data must drive the decision on whether or not to include various data sets. The other complicating factor here is that many sources for Big Data are unstructured, making the detection of potentially sensitive or proprietary information even more difficult. As companies evolve their Big Data data sets, they will need to involve legal and general counsel.

Xiong: Protecting an individual’s privacy and ensuring that a third party’s intellectual property rights are not infringed is critical. These aspects need to be safeguarded during every step of Big Data analytics. This includes data collection, data storage, data analysis and the execution of business strategies that are derived from Big Data projects. Having a transparent privacy policy and frequent communication with consumers about how their data is collected and used is in the best interest of any organisation. This should be an essential part of any Big Data initiative. When in doubt, consult your legal and compliance organisations.

Elliott: It is critical to understand the legal right to use data that is being accessed by the various data service providers. Aside from the potential privacy issues associated with collecting data from audio, video and log analysis, many services such as web scraping are still being debated in courts and are being challenged as directly violating of terms of use. To reduce exposure, a company must have well-defined and functioning data governance collaboration between business, IT and legal leaders. Additionally, it is critical to manage the numerous point solution providers across the enterprise that are using or providing information as a service. Their oversight can pass liability to the company and expose the company to litigation.

There is a risk that the ability to collect data is outpacing the understanding of how to do so responsibly.

— Ken Elliott

FW: In your opinion, when businesses adopt a potentially disruptive technology such as Big Data analytics, is there a chance they will fail to identify all the risks that need to be managed? How should companies address the legal and regulatory scrutiny surrounding data usage?

Xiong: Like any disruptive technology, Big Data analytics has risks and every business needs to ensure they identify and manage those potential risks. By managing them, organisations will be able to minimise any potentially negative impact on their business. The most common risk is underestimating the investment and complexity of a Big Data initiative. The second risk is not properly protecting an individual’s privacy, and the third is aggressively implementing a business strategy derived from Big Data analytics without proper testing. As long as privacy rights are respected, vigorous security measures are in place to protect personal information, compliance protocols are carefully maintained and there remains a total commitment to data accuracy, the opportunities brought by Big Data should not be hindered.

Elliott: Big Data has risen from the relatively recent expansion of the capability to store and process a greater variety and volume of data. As a result there has been an explosion of new sources, applications and devices that collect potentially private and proprietary information. There is a risk that the ability to collect data is outpacing the understanding of how to do so responsibly. This includes the collection, management, usage, security and archiving of potentially sensitive information. To ensure legal compliance, companies should establish formal data governance, document data management policies and procedures, establish an audit and review process and seek consultation from information governance professionals.

Dil: With all the disruptive changes in the business environment today, including from new technologies, risk is constantly on top of corporate agendas, whether it be underestimating risks or the failure to properly align initial investment needs, understand business drivers or recognise a deteriorating business model. Understanding the critical assumptions underlying the corporate strategy, conducting contrarian analysis with those assumptions, identifying the vital signs in the business environment that would indicate whether one or more critical assumptions are either no longer valid or becoming invalid, and aligning intelligence gathering to focus on those vital signs, are ways to identify and monitor potentially disruptive risks. Data governance is another solution, but certainly not the silver bullet to cure all woes. Companies also need to focus on compliance with local statutory laws and regulations in the various jurisdictions in which they operate, many of which restrict the collection, handling and transfer of sensitive data.

FW: To what extent are businesses building on their use of Big Data analytics to embrace Smart Data, which purports to filter out the ‘noise’ and identify valuable data? Do you believe more businesses will adopt the Smart Data approach?

Xiong: We are, by and large, better when we can make sense of the world around us, and that world is being made more complex by the vast amount of information that’s out there. As the volume of data increases, it has become more challenging to identify and extract useful information or business intelligence from raw data. This can be like finding a needle in a haystack. In this sense, the data analyst has embraced Smart Data. Many advanced algorithms and software tools have been developed to help filter out the noise by analysing and visualising the data. This has helped businesses adopt the Smart Data approach in order to really benefit from Big Data analytics.

Dil: The concept of Smart Data has been around since the initial advent of management reporting and decision support systems, so this is not a new demand; rather, it’s applying an older data management discipline to a new source of information flowing from Big Data initiatives. Even though hardware and software advances have made it cheaper to collect large sets of data, including the added ‘noise’, there are still fundamental costs to maintaining this data, including added time for analysis, and potential e-discovery or retention risks. As such, the need to continue to shrink data sets, even those defined as Big Data sets, will continue to drive organisations.

Elliott: Extracting value from Big Data requires more efficient means of collecting and managing data, and most importantly analysing that data. The first part of the solution is to make Big Data available for analysis using cost effective means. Following this, shifting out the noise and identifying relevant data requires data mining and statistical techniques which can process massive amounts of data and reveal precisely which data elements are predictive or descriptive of business outcomes. Using analytics in this way further enables business intelligence development to focus on the Smart Data which is most relevant to business decision making.

The productivity increase from Big Data analytics will help us use data for good by benefiting people, our society and our economy.

— Shanji Xiong

FW: What trends and developments in the Big Data analytics sphere do you expect to see in the coming years? In what ways do you believe this trend will transform business practices?

Elliott: While a handful of data centric companies such as LinkedIn, Google and eBay have led the way, most others are still either experimenting with Big Data or planning their Big Data strategy. According to Gartner, through 2015, 85 percent of Fortune 500 organisations will be unable to exploit Big Data for competitive advantage. With limited capital investment and skilled resources, many companies are turning to third party Big Data discovery platforms as a quick way to validate and test their use cases. Given the rapidly evolving nature of Big Data techniques and technology, this trend toward service platforms is extending to more permanent Big Data platforms as a service. Pursuing Big Data platforms as a service allows organisations and IT to focus on their core business while enjoying more rapid insights at a lower total cost of ownership and much lower risk. Within these platforms, innovation in the Big Data analytics sphere is moving toward the expanded use of machine learning for automated analytics and integration with decision management systems to shorten the distance between Big Data and business results.

Xiong: Over the last several years, organisations have invested significantly in data collection, storage and analytical platforms. In the future, their focus will be on developing impactful analytical intelligence and applying it to business processes. Data scientists with business acumen and solid analytical capability will play an instrumental role in this process. This presents tremendous opportunities for data scientists to have a positive impact on business and society. Powered by Big Data analytics, business will happen more in real-time and be tailored for individuals. Examples include consumers being able to design their own car online or having their medicine customised for their specific needs and delivered to them even before they know they need it. The productivity increase from Big Data analytics will help us use data for good by benefiting people, our society and our economy.

Dil: Organisations are just beginning to take advantage of combining their internal data sets with external data, despite the risks. We believe this trend will continue for years to come, with more high-quality external data sets becoming commodities to assist in the analysis of real-world problems. These data sets might originate from entirely new sources, such as devices participating in the Internet of Things, to improve the ability of organisations to understand customers, competitors and performance improvement opportunities and to improve quality, compress time and reduce costs of providing goods and services. As data availability, consumption and analytics become ‘real time’, the transformation of business practices will evolve as businesses become better at understanding how best to leverage these new data sources for increasing value through predictive analytics.

Dr Shanji Xiong is the chief scientist of Experian’s DataLabs. Prior to his current role, he held senior positions with Morgan Stanley, FICO, HNC and ID Analytics. For the past 20 years he has been working in the Big Data area, developing analytical solutions for financial, telecommunication and insurance companies. Dr Xiong received his doctoral degree from Columbia University in Engineering Mechanics. He can be contacted on +1 (714) 830 7475 or by email: shanji.xiong@experian.com.

Ken Elliott, Ph.D. is director of analytics within HP’s Analytics and Data Management arm at HP Enterprise Services. In his role, he effectively combines strategic thinking, leadership, analytic knowledge and technology to business process improvements, which deliver measurable corporate results. He has more than 25 years of experience delivering business intelligence and analytic solutions, which improve corporate performance. Mr Elliott holds a Ph.D. in Industrial Psychology with a focus on analytics. He can be contacted on +1 (512) 319 7355 or by email: kenneth.elliott@hp.com.

Shaheen Dil is a managing director with Protiviti and is responsible for the Data Management & Advanced Analytics Solution. Ms Dil has more than 25 years of experience in all aspects of domestic and international risk management, including Basel qualification and compliance, capital management and stress testing for CCAR and DFAST, enterprise-wide risk governance and reporting, risk modelling and model validation, credit approvals and credit portfolio management. She can be contacted on +1 (212) 603 8378 or by email: shaheen.dil@protiviti.com.